Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations388878
Missing cells2297089
Missing cells (%)29.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.3 MiB
Average record size in memory160.0 B

Variable types

Text6
Numeric8
Categorical5
Boolean1

Alerts

defense_coverage_type is highly overall correlated with defense_man_zone_type and 1 other fieldsHigh correlation
defense_man_zone_type is highly overall correlated with defense_coverage_type and 1 other fieldsHigh correlation
n_defense is highly overall correlated with n_offenseHigh correlation
n_offense is highly overall correlated with defense_coverage_type and 2 other fieldsHigh correlation
possession_team has 36092 (9.3%) missing valuesMissing
offense_formation has 108106 (27.8%) missing valuesMissing
offense_personnel has 96104 (24.7%) missing valuesMissing
defenders_in_box has 103119 (26.5%) missing valuesMissing
defense_personnel has 96104 (24.7%) missing valuesMissing
number_of_pass_rushers has 223946 (57.6%) missing valuesMissing
players_on_play has 36152 (9.3%) missing valuesMissing
offense_players has 36160 (9.3%) missing valuesMissing
defense_players has 36152 (9.3%) missing valuesMissing
ngs_air_yards has 243814 (62.7%) missing valuesMissing
time_to_throw has 240418 (61.8%) missing valuesMissing
was_pressure has 240432 (61.8%) missing valuesMissing
route has 245106 (63.0%) missing valuesMissing
defense_man_zone_type has 277692 (71.4%) missing valuesMissing
defense_coverage_type has 277692 (71.4%) missing valuesMissing
n_offense has 36160 (9.3%) zerosZeros
n_defense has 36152 (9.3%) zerosZeros

Reproduction

Analysis started2024-08-09 02:29:07.211984
Analysis finished2024-08-09 02:29:35.072455
Duration27.86 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct2153
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:35.232754image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length15
Median length15
Mean length14.506498
Min length13

Characters and Unicode

Total characters5641258
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016_01_CAR_DEN
2nd row2016_01_CAR_DEN
3rd row2016_01_CAR_DEN
4th row2016_01_CAR_DEN
5th row2016_01_CAR_DEN
ValueCountFrequency (%)
2019_01_det_ari 252
 
0.1%
2019_16_cin_mia 248
 
0.1%
2022_15_ind_min 243
 
0.1%
2018_04_hou_ind 241
 
0.1%
2018_04_cle_oak 240
 
0.1%
2017_04_sf_ari 236
 
0.1%
2016_08_was_cin 234
 
0.1%
2016_12_kc_den 234
 
0.1%
2018_01_pit_cle 234
 
0.1%
2020_07_sea_ari 234
 
0.1%
Other values (2143) 386482
99.4%
2024-08-08T20:29:35.530732image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 1166634
20.7%
2 688058
12.2%
0 656692
11.6%
1 475556
 
8.4%
A 273392
 
4.8%
N 216154
 
3.8%
I 193455
 
3.4%
L 155527
 
2.8%
E 145016
 
2.6%
C 142984
 
2.5%
Other values (25) 1527790
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5641258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 1166634
20.7%
2 688058
12.2%
0 656692
11.6%
1 475556
 
8.4%
A 273392
 
4.8%
N 216154
 
3.8%
I 193455
 
3.4%
L 155527
 
2.8%
E 145016
 
2.6%
C 142984
 
2.5%
Other values (25) 1527790
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5641258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 1166634
20.7%
2 688058
12.2%
0 656692
11.6%
1 475556
 
8.4%
A 273392
 
4.8%
N 216154
 
3.8%
I 193455
 
3.4%
L 155527
 
2.8%
E 145016
 
2.6%
C 142984
 
2.5%
Other values (25) 1527790
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5641258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 1166634
20.7%
2 688058
12.2%
0 656692
11.6%
1 475556
 
8.4%
A 273392
 
4.8%
N 216154
 
3.8%
I 193455
 
3.4%
L 155527
 
2.8%
E 145016
 
2.6%
C 142984
 
2.5%
Other values (25) 1527790
27.1%

old_game_id
Real number (ℝ)

Distinct2153
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0196999 × 109
Minimum2.0160908 × 109
Maximum2.0240211 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:35.651102image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.0160908 × 109
5-th percentile2.0161024 × 109
Q12.0180909 × 109
median2.0200913 × 109
Q32.0220908 × 109
95-th percentile2.0231112 × 109
Maximum2.0240211 × 109
Range7930300
Interquartile range (IQR)3999898

Descriptive statistics

Standard deviation2314464.3
Coefficient of variation (CV)0.0011459446
Kurtosis-1.2499567
Mean2.0196999 × 109
Median Absolute Deviation (MAD)1999802
Skewness-0.012194877
Sum7.8541687 × 1014
Variance5.3567448 × 1012
MonotonicityNot monotonic
2024-08-08T20:29:35.785223image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019090809 252
 
0.1%
2019122208 248
 
0.1%
2022121700 243
 
0.1%
2018093004 241
 
0.1%
2018093009 240
 
0.1%
2017100109 236
 
0.1%
2020102508 234
 
0.1%
2018090901 234
 
0.1%
2016103000 234
 
0.1%
2016112709 234
 
0.1%
Other values (2143) 386482
99.4%
ValueCountFrequency (%)
2016090800 174
< 0.1%
2016091100 178
< 0.1%
2016091101 159
< 0.1%
2016091102 179
< 0.1%
2016091103 187
< 0.1%
2016091104 202
0.1%
2016091105 195
0.1%
2016091106 173
< 0.1%
2016091107 174
< 0.1%
2016091108 175
< 0.1%
ValueCountFrequency (%)
2024021100 225
0.1%
2024012801 200
0.1%
2024012800 192
< 0.1%
2024012101 182
< 0.1%
2024012100 192
< 0.1%
2024012001 184
< 0.1%
2024012000 184
< 0.1%
2024011500 186
< 0.1%
2024011402 166
< 0.1%
2024011401 214
0.1%

play_id
Real number (ℝ)

Distinct5338
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2134.0654
Minimum1
Maximum5921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:35.913340image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile215
Q11064
median2114
Q33180
95-th percentile4091
Maximum5921
Range5920
Interquartile range (IQR)2116

Descriptive statistics

Standard deviation1246.2403
Coefficient of variation (CV)0.58397472
Kurtosis-1.0839628
Mean2134.0654
Median Absolute Deviation (MAD)1058
Skewness0.071231641
Sum8.2989109 × 108
Variance1553114.8
MonotonicityNot monotonic
2024-08-08T20:29:36.032450image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2153
 
0.6%
36 767
 
0.2%
40 405
 
0.1%
55 370
 
0.1%
51 338
 
0.1%
41 290
 
0.1%
39 267
 
0.1%
79 208
 
0.1%
56 201
 
0.1%
58 186
 
< 0.1%
Other values (5328) 383693
98.7%
ValueCountFrequency (%)
1 2153
0.6%
35 38
 
< 0.1%
36 767
 
0.2%
37 125
 
< 0.1%
38 23
 
< 0.1%
39 267
 
0.1%
40 405
 
0.1%
41 290
 
0.1%
42 100
 
< 0.1%
43 25
 
< 0.1%
ValueCountFrequency (%)
5921 1
< 0.1%
5899 1
< 0.1%
5876 1
< 0.1%
5854 1
< 0.1%
5832 1
< 0.1%
5813 1
< 0.1%
5796 1
< 0.1%
5772 1
< 0.1%
5748 1
< 0.1%
5729 1
< 0.1%

possession_team
Categorical

MISSING 

Distinct34
Distinct (%)< 0.1%
Missing36092
Missing (%)9.3%
Memory size3.0 MiB
KC
 
12386
PHI
 
11882
NE
 
11683
TB
 
11651
BAL
 
11572
Other values (29)
293612 

Length

Max length3
Median length3
Mean length2.7512685
Min length2

Characters and Unicode

Total characters970609
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCAR
2nd rowDEN
3rd rowDEN
4th rowDEN
5th rowDEN

Common Values

ValueCountFrequency (%)
KC 12386
 
3.2%
PHI 11882
 
3.1%
NE 11683
 
3.0%
TB 11651
 
3.0%
BAL 11572
 
3.0%
LA 11533
 
3.0%
BUF 11526
 
3.0%
DAL 11515
 
3.0%
SF 11432
 
2.9%
GB 11238
 
2.9%
Other values (24) 236368
60.8%
(Missing) 36092
 
9.3%

Length

2024-08-08T20:29:36.147559image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kc 12386
 
3.5%
phi 11882
 
3.4%
ne 11683
 
3.3%
tb 11651
 
3.3%
bal 11572
 
3.3%
la 11533
 
3.3%
buf 11526
 
3.3%
dal 11515
 
3.3%
sf 11432
 
3.2%
gb 11238
 
3.2%
Other values (24) 236368
67.0%

Most occurring characters

ValueCountFrequency (%)
A 124317
12.8%
N 97661
 
10.1%
I 87363
 
9.0%
L 71186
 
7.3%
E 65492
 
6.7%
C 64647
 
6.7%
T 55196
 
5.7%
B 45987
 
4.7%
D 45001
 
4.6%
S 34119
 
3.5%
Other values (14) 279640
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 970609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 124317
12.8%
N 97661
 
10.1%
I 87363
 
9.0%
L 71186
 
7.3%
E 65492
 
6.7%
C 64647
 
6.7%
T 55196
 
5.7%
B 45987
 
4.7%
D 45001
 
4.6%
S 34119
 
3.5%
Other values (14) 279640
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 970609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 124317
12.8%
N 97661
 
10.1%
I 87363
 
9.0%
L 71186
 
7.3%
E 65492
 
6.7%
C 64647
 
6.7%
T 55196
 
5.7%
B 45987
 
4.7%
D 45001
 
4.6%
S 34119
 
3.5%
Other values (14) 279640
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 970609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 124317
12.8%
N 97661
 
10.1%
I 87363
 
9.0%
L 71186
 
7.3%
E 65492
 
6.7%
C 64647
 
6.7%
T 55196
 
5.7%
B 45987
 
4.7%
D 45001
 
4.6%
S 34119
 
3.5%
Other values (14) 279640
28.8%

offense_formation
Categorical

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing108106
Missing (%)27.8%
Memory size3.0 MiB
SHOTGUN
151986 
SINGLEBACK
68453 
EMPTY
23816 
I_FORM
23310 
PISTOL
 
9118
Other values (2)
 
4089

Length

Max length10
Median length7
Mean length7.425224
Min length5

Characters and Unicode

Total characters2084795
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSINGLEBACK
2nd rowI_FORM
3rd rowSINGLEBACK
4th rowSHOTGUN
5th rowSINGLEBACK

Common Values

ValueCountFrequency (%)
SHOTGUN 151986
39.1%
SINGLEBACK 68453
17.6%
EMPTY 23816
 
6.1%
I_FORM 23310
 
6.0%
PISTOL 9118
 
2.3%
JUMBO 2954
 
0.8%
WILDCAT 1135
 
0.3%
(Missing) 108106
27.8%

Length

2024-08-08T20:29:36.249652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:29:36.357444image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
shotgun 151986
54.1%
singleback 68453
24.4%
empty 23816
 
8.5%
i_form 23310
 
8.3%
pistol 9118
 
3.2%
jumbo 2954
 
1.1%
wildcat 1135
 
0.4%

Most occurring characters

ValueCountFrequency (%)
S 229557
11.0%
G 220439
10.6%
N 220439
10.6%
O 187368
9.0%
T 186055
8.9%
U 154940
 
7.4%
H 151986
 
7.3%
I 102016
 
4.9%
E 92269
 
4.4%
L 78706
 
3.8%
Other values (13) 461020
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2084795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 229557
11.0%
G 220439
10.6%
N 220439
10.6%
O 187368
9.0%
T 186055
8.9%
U 154940
 
7.4%
H 151986
 
7.3%
I 102016
 
4.9%
E 92269
 
4.4%
L 78706
 
3.8%
Other values (13) 461020
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2084795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 229557
11.0%
G 220439
10.6%
N 220439
10.6%
O 187368
9.0%
T 186055
8.9%
U 154940
 
7.4%
H 151986
 
7.3%
I 102016
 
4.9%
E 92269
 
4.4%
L 78706
 
3.8%
Other values (13) 461020
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2084795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 229557
11.0%
G 220439
10.6%
N 220439
10.6%
O 187368
9.0%
T 186055
8.9%
U 154940
 
7.4%
H 151986
 
7.3%
I 102016
 
4.9%
E 92269
 
4.4%
L 78706
 
3.8%
Other values (13) 461020
22.1%

offense_personnel
Text

MISSING 

Distinct291
Distinct (%)0.1%
Missing96104
Missing (%)24.7%
Memory size3.0 MiB
2024-08-08T20:29:36.468375image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length41
Median length16
Mean length16.29131
Min length16

Characters and Unicode

Total characters4769672
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)< 0.1%

Sample

1st row1 RB, 1 TE, 3 WR
2nd row6 OL, 2 RB, 0 TE, 2 WR
3rd row1 RB, 1 TE, 3 WR
4th row1 RB, 1 TE, 3 WR
5th row1 RB, 0 TE, 4 WR
ValueCountFrequency (%)
1 481981
27.0%
rb 292774
16.4%
te 292774
16.4%
wr 291330
16.3%
3 190934
 
10.7%
2 185097
 
10.4%
0 14929
 
0.8%
ol 10790
 
0.6%
6 10409
 
0.6%
4 6505
 
0.4%
Other values (36) 6020
 
0.3%
2024-08-08T20:29:36.677029image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1490769
31.3%
, 600282
12.6%
R 585548
 
12.3%
1 484103
 
10.1%
B 295146
 
6.2%
T 292774
 
6.1%
E 292774
 
6.1%
W 292774
 
6.1%
3 191090
 
4.0%
2 185266
 
3.9%
Other values (13) 59146
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4769672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1490769
31.3%
, 600282
12.6%
R 585548
 
12.3%
1 484103
 
10.1%
B 295146
 
6.2%
T 292774
 
6.1%
E 292774
 
6.1%
W 292774
 
6.1%
3 191090
 
4.0%
2 185266
 
3.9%
Other values (13) 59146
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4769672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1490769
31.3%
, 600282
12.6%
R 585548
 
12.3%
1 484103
 
10.1%
B 295146
 
6.2%
T 292774
 
6.1%
E 292774
 
6.1%
W 292774
 
6.1%
3 191090
 
4.0%
2 185266
 
3.9%
Other values (13) 59146
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4769672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1490769
31.3%
, 600282
12.6%
R 585548
 
12.3%
1 484103
 
10.1%
B 295146
 
6.2%
T 292774
 
6.1%
E 292774
 
6.1%
W 292774
 
6.1%
3 191090
 
4.0%
2 185266
 
3.9%
Other values (13) 59146
 
1.2%

defenders_in_box
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing103119
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean6.3706515
Minimum0
Maximum11
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:36.767112image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q16
median6
Q37
95-th percentile8
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0835303
Coefficient of variation (CV)0.17008155
Kurtosis1.2873542
Mean6.3706515
Median Absolute Deviation (MAD)1
Skewness0.173616
Sum1820471
Variance1.1740378
MonotonicityNot monotonic
2024-08-08T20:29:36.860155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 118904
30.6%
7 81475
21.0%
5 35700
 
9.2%
8 32343
 
8.3%
4 9982
 
2.6%
9 3938
 
1.0%
3 1343
 
0.3%
10 1100
 
0.3%
11 797
 
0.2%
2 140
 
< 0.1%
Other values (2) 37
 
< 0.1%
(Missing) 103119
26.5%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 32
 
< 0.1%
2 140
 
< 0.1%
3 1343
 
0.3%
4 9982
 
2.6%
5 35700
 
9.2%
6 118904
30.6%
7 81475
21.0%
8 32343
 
8.3%
9 3938
 
1.0%
ValueCountFrequency (%)
11 797
 
0.2%
10 1100
 
0.3%
9 3938
 
1.0%
8 32343
 
8.3%
7 81475
21.0%
6 118904
30.6%
5 35700
 
9.2%
4 9982
 
2.6%
3 1343
 
0.3%
2 140
 
< 0.1%

defense_personnel
Text

MISSING 

Distinct287
Distinct (%)0.1%
Missing96104
Missing (%)24.7%
Memory size3.0 MiB
2024-08-08T20:29:36.968261image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length42
Median length16
Mean length16.01703
Min length16

Characters and Unicode

Total characters4689370
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)< 0.1%

Sample

1st row4 DL, 2 LB, 5 DB
2nd row4 DL, 3 LB, 4 DB
3rd row4 DL, 2 LB, 5 DB
4th row4 DL, 2 LB, 5 DB
5th row4 DL, 2 LB, 5 DB
ValueCountFrequency (%)
lb 292774
16.7%
db 292774
16.7%
dl 292774
16.7%
4 292085
16.6%
3 186819
10.6%
5 181932
10.3%
2 155707
8.9%
6 38652
 
2.2%
1 20266
 
1.2%
7 2465
 
0.1%
Other values (20) 1915
 
0.1%
2024-08-08T20:29:37.177450image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1465389
31.2%
, 586415
12.5%
B 585857
 
12.5%
L 585678
 
12.5%
D 585548
 
12.5%
4 292086
 
6.2%
3 186838
 
4.0%
5 181933
 
3.9%
2 155778
 
3.3%
6 38652
 
0.8%
Other values (12) 25196
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4689370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1465389
31.2%
, 586415
12.5%
B 585857
 
12.5%
L 585678
 
12.5%
D 585548
 
12.5%
4 292086
 
6.2%
3 186838
 
4.0%
5 181933
 
3.9%
2 155778
 
3.3%
6 38652
 
0.8%
Other values (12) 25196
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4689370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1465389
31.2%
, 586415
12.5%
B 585857
 
12.5%
L 585678
 
12.5%
D 585548
 
12.5%
4 292086
 
6.2%
3 186838
 
4.0%
5 181933
 
3.9%
2 155778
 
3.3%
6 38652
 
0.8%
Other values (12) 25196
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4689370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1465389
31.2%
, 586415
12.5%
B 585857
 
12.5%
L 585678
 
12.5%
D 585548
 
12.5%
4 292086
 
6.2%
3 186838
 
4.0%
5 181933
 
3.9%
2 155778
 
3.3%
6 38652
 
0.8%
Other values (12) 25196
 
0.5%

number_of_pass_rushers
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)< 0.1%
Missing223946
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean4.2341753
Minimum0
Maximum10
Zeros742
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:37.266533image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median4
Q35
95-th percentile6
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8797653
Coefficient of variation (CV)0.20777725
Kurtosis3.9744711
Mean4.2341753
Median Absolute Deviation (MAD)0
Skewness0.02919235
Sum698351
Variance0.77398699
MonotonicityNot monotonic
2024-08-08T20:29:37.359083image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 101432
26.1%
5 34263
 
8.8%
3 13628
 
3.5%
6 9723
 
2.5%
7 2133
 
0.5%
2 2102
 
0.5%
0 742
 
0.2%
1 621
 
0.2%
8 266
 
0.1%
9 18
 
< 0.1%
(Missing) 223946
57.6%
ValueCountFrequency (%)
0 742
 
0.2%
1 621
 
0.2%
2 2102
 
0.5%
3 13628
 
3.5%
4 101432
26.1%
5 34263
 
8.8%
6 9723
 
2.5%
7 2133
 
0.5%
8 266
 
0.1%
9 18
 
< 0.1%
ValueCountFrequency (%)
10 4
 
< 0.1%
9 18
 
< 0.1%
8 266
 
0.1%
7 2133
 
0.5%
6 9723
 
2.5%
5 34263
 
8.8%
4 101432
26.1%
3 13628
 
3.5%
2 2102
 
0.5%
1 621
 
0.2%

players_on_play
Text

MISSING 

Distinct318127
Distinct (%)90.2%
Missing36152
Missing (%)9.3%
Memory size3.0 MiB
2024-08-08T20:29:37.839948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length245
Median length131
Mean length130.98598
Min length77

Characters and Unicode

Total characters46202162
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique292166 ?
Unique (%)82.8%

Sample

1st row41285;42728;39857;43366;35656;42512;41916;40096;44017;34659;36494;41915;37841;35638;35102;43465;41471;43508;35685;42727;43387;37265
2nd row35521;42593;41377;35685;35461;41289;35690;38539;38604;41324;42476;41357;29839;34961;32276;35445;40311;43351;39960;39992;41436;37279
3rd row42368;41377;42593;35521;41285;35685;41289;43465;38539;38604;41324;42476;41357;29839;39027;32276;35445;40311;43351;39960;39992;41436
4th row41285;42476;43351;38539;39992;38604;39960;41377;40311;35521;41436;35445;41357;41324;35685;32276;29839;35690;42593;34961;41289;37279
5th row41285;43351;42476;38539;39992;39960;38604;41377;40311;37841;35521;41436;35445;41357;35685;41324;32276;29839;35690;42593;34961;37279
ValueCountFrequency (%)
38540;44923;42375;35466;43850;42410;42444;43503;34485;44816;42006;44846;41229;41269;41268;30842;35067;41322;41659;42390;40346;37308 13
 
< 0.1%
35456;45281;46081;43300;47877;45062;47847;46186;42412;47788;38605;43757;47822;45039;43504;35441;33107;39957;43320;41915;38588;35454 13
 
< 0.1%
42400;53505;54629;43399;46376;37097;56042;56011;53868;38607;44881;53489;40017;54514;34452;43415;46168;48537;47867;46141;55901;52543 13
 
< 0.1%
42400;43300;46180;43399;43497;41483;47853;46158;46255;46672;47952;44881;52465;52498;34452;41300;39989;46102;53624;46137;43356;47837 13
 
< 0.1%
39974;43290;34639;43399;33268;42513;41230;41380;42400;44881;39989;30869;43353;39981;33341;37152;42415;44830;35455;42353;34464;35472 11
 
< 0.1%
47814;46263;47785;46394;44815;41232;46253;44878;42360;43296;34472;43370;43484;46139;46211;33084;46354;47797;37079;41332;47931;41256 11
 
< 0.1%
47889;41270;44876;52427;45345;43808;44820;46093;47823;46156;41436;43356;35445;41258;47787;41324;44852;39908;43797;46170;42424;47810 10
 
< 0.1%
42492;43378;32318;34530;41292;43302;42365;27498;40092;41245;40054;34498;41338;32363;33096;32853;32208;43314;41241;43347;41275;42357 10
 
< 0.1%
42500;43045;44968;38569;52588;46221;42031;43344;47985;47794;41619;52469;43353;52409;52441;47802;53434;47899;37212;42365;46110;44927 10
 
< 0.1%
44903;38757;37146;41270;35526;40494;43045;42816;43297;39976;38595;40445;41237;42345;38629;43797;30990;37087;41239;38544;34497;44817 9
 
< 0.1%
Other values (318117) 352613
> 99.9%
2024-08-08T20:29:38.309242image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 8696836
18.8%
; 7406422
16.0%
3 5430296
11.8%
5 4305968
9.3%
2 3586701
7.8%
6 2963230
 
6.4%
8 2955420
 
6.4%
7 2862873
 
6.2%
1 2794198
 
6.0%
0 2619319
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46202162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 8696836
18.8%
; 7406422
16.0%
3 5430296
11.8%
5 4305968
9.3%
2 3586701
7.8%
6 2963230
 
6.4%
8 2955420
 
6.4%
7 2862873
 
6.2%
1 2794198
 
6.0%
0 2619319
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46202162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 8696836
18.8%
; 7406422
16.0%
3 5430296
11.8%
5 4305968
9.3%
2 3586701
7.8%
6 2963230
 
6.4%
8 2955420
 
6.4%
7 2862873
 
6.2%
1 2794198
 
6.0%
0 2619319
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46202162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 8696836
18.8%
; 7406422
16.0%
3 5430296
11.8%
5 4305968
9.3%
2 3586701
7.8%
6 2963230
 
6.4%
8 2955420
 
6.4%
7 2862873
 
6.2%
1 2794198
 
6.0%
0 2619319
 
5.7%

offense_players
Text

MISSING 

Distinct143644
Distinct (%)40.7%
Missing36160
Missing (%)9.3%
Memory size3.0 MiB
2024-08-08T20:29:38.547992image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length230
Median length120
Mean length119.99938
Min length54

Characters and Unicode

Total characters42325940
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84890 ?
Unique (%)24.1%

Sample

1st row00-0029731;00-0032891;00-0031596;00-0030463;00-0032994;00-0027853;00-0028339;00-0027796;00-0026858;00-0027835;00-0028128
2nd row00-0027685;00-0032156;00-0027874;00-0029543;00-0031353;00-0032132;00-0026739;00-0027859;00-0029854;00-0031349;00-0028142
3rd row00-0032156;00-0027685;00-0031344;00-0032956;00-0029543;00-0031353;00-0032132;00-0027859;00-0029854;00-0031349
4th row00-0031344;00-0032132;00-0029543;00-0029854;00-0027685;00-0031349;00-0027859;00-0031353;00-0032156;00-0026739;00-0028142
5th row00-0031344;00-0032132;00-0029543;00-0029854;00-0027685;00-0031349;00-0027859;00-0031353;00-0032156;00-0026739;00-0028142
ValueCountFrequency (%)
00-0033921;00-0034668;00-0036406;00-0039157;00-0038129;00-0031408;00-0036985;00-0034855;00-0039052;00-0037835;00-0037256 267
 
0.1%
00-0033106;00-0033110;00-0026327;00-0031325;00-0032242;00-0033908;00-0030431;00-0024270;00-0032244;00-0032241;00-0027648 213
 
0.1%
00-0032242;00-0034804;00-0033110;00-0035664;00-0035668;00-0033908;00-0036415;00-0026498;00-0024270;00-0030431;00-0034261 206
 
0.1%
00-0033106;00-0033110;00-0026327;00-0032455;00-0031236;00-0032242;00-0024270;00-0030431;00-0033943;00-0032241;00-0027648 181
 
0.1%
00-0033077;00-0038041;00-0034764;00-0035261;00-0036358;00-0036376;00-0036036;00-0037243;00-0027947;00-0031402;00-0031236 178
 
0.1%
00-0033106;00-0033110;00-0026327;00-0032455;00-0031236;00-0032242;00-0033908;00-0024270;00-0030431;00-0032241;00-0027648 159
 
< 0.1%
00-0030827;00-0037814;00-0033881;00-0034775;00-0029293;00-0035237;00-0032053;00-0033889;00-0036973;00-0033891;00-0036971 152
 
< 0.1%
00-0030542;00-0030546;00-0029010;00-0027947;00-0025509;00-0029000;00-0027902;00-0033077;00-0022127;00-0031402;00-0033045 137
 
< 0.1%
00-0032134;00-0032043;00-0036310;00-0033009;00-0035295;00-0035629;00-0030781;00-0036442;00-0036410;00-0036900;00-0033897 136
 
< 0.1%
00-0031306;00-0027973;00-0032233;00-0032134;00-0033009;00-0024270;00-0027681;00-0029592;00-0028039;00-0030456;00-0027942 125
 
< 0.1%
Other values (143634) 350964
99.5%
2024-08-08T20:29:38.904075image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16899348
39.9%
3 4702425
 
11.1%
- 3879878
 
9.2%
; 3527160
 
8.3%
2 2646185
 
6.3%
6 1669060
 
3.9%
1 1595113
 
3.8%
4 1571111
 
3.7%
5 1557280
 
3.7%
9 1533811
 
3.6%
Other values (2) 2744569
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42325940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16899348
39.9%
3 4702425
 
11.1%
- 3879878
 
9.2%
; 3527160
 
8.3%
2 2646185
 
6.3%
6 1669060
 
3.9%
1 1595113
 
3.8%
4 1571111
 
3.7%
5 1557280
 
3.7%
9 1533811
 
3.6%
Other values (2) 2744569
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42325940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16899348
39.9%
3 4702425
 
11.1%
- 3879878
 
9.2%
; 3527160
 
8.3%
2 2646185
 
6.3%
6 1669060
 
3.9%
1 1595113
 
3.8%
4 1571111
 
3.7%
5 1557280
 
3.7%
9 1533811
 
3.6%
Other values (2) 2744569
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42325940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16899348
39.9%
3 4702425
 
11.1%
- 3879878
 
9.2%
; 3527160
 
8.3%
2 2646185
 
6.3%
6 1669060
 
3.9%
1 1595113
 
3.8%
4 1571111
 
3.7%
5 1557280
 
3.7%
9 1533811
 
3.6%
Other values (2) 2744569
 
6.5%

defense_players
Text

MISSING 

Distinct172835
Distinct (%)49.0%
Missing36152
Missing (%)9.3%
Memory size3.0 MiB
2024-08-08T20:29:39.147583image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length241
Median length120
Mean length119.93117
Min length87

Characters and Unicode

Total characters42302843
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105555 ?
Unique (%)29.9%

Sample

1st row00-0031344;00-0031697;00-0027812;00-0031102;00-0027139;00-0031101;00-0032956;00-0031350;00-0032958;00-0031696;00-0032971
2nd row00-0031262;00-0027835;00-0031336;00-0027840;00-0029248;00-0031261;00-0023448;00-0025470;00-0032890;00-0030455;00-0030448
3rd row00-0031550;00-0031262;00-0027835;00-0031336;00-0029248;00-0031261;00-0023448;00-0025470;00-0032890;00-0030455;00-0030448
4th row00-0032890;00-0029248;00-0030448;00-0030455;00-0031262;00-0031261;00-0027835;00-0025470;00-0023448;00-0027840;00-0031336
5th row00-0032890;00-0029248;00-0030448;00-0030455;00-0031262;00-0028339;00-0031261;00-0027835;00-0025470;00-0023448;00-0027840
ValueCountFrequency (%)
00-0032968;00-0026190;00-0031259;00-0030228;00-0029568;00-0032052;00-0027416;00-0033055;00-0028924;00-0031309;00-0033123 143
 
< 0.1%
00-0030562;00-0031583;00-0027958;00-0030528;00-0032240;00-0031181;00-0032382;00-0032401;00-0032596;00-0032388;00-0029688 138
 
< 0.1%
00-0034834;00-0032379;00-0033548;00-0034675;00-0033905;00-0035697;00-0033935;00-0032939;00-0032521;00-0036294;00-0032424 119
 
< 0.1%
00-0033109;00-0032146;00-0020712;00-0027023;00-0031016;00-0025557;00-0025402;00-0033103;00-0031365;00-0033053;00-0031412 95
 
< 0.1%
00-0035933;00-0039137;00-0035942;00-0033783;00-0037839;00-0039138;00-0037842;00-0031388;00-0036994;00-0037844;00-0036431 91
 
< 0.1%
00-0034834;00-0032379;00-0033548;00-0034675;00-0035697;00-0033935;00-0032939;00-0032521;00-0036294;00-0034800;00-0032424 88
 
< 0.1%
00-0031170;00-0029272;00-0026990;00-0032803;00-0027882;00-0027865;00-0028899;00-0032195;00-0029409;00-0029653;00-0029412 83
 
< 0.1%
00-0033927;00-0031389;00-0030041;00-0032165;00-0032052;00-0029670;00-0032677;00-0026160;00-0027855;00-0031388;00-0029630 82
 
< 0.1%
00-0030562;00-0031583;00-0027958;00-0030528;00-0031181;00-0032382;00-0032401;00-0033540;00-0032596;00-0032388;00-0029688 77
 
< 0.1%
00-0036937;00-0027962;00-0029607;00-0036927;00-0030459;00-0038121;00-0033892;00-0033874;00-0035402;00-0035656;00-0034760 76
 
< 0.1%
Other values (172825) 351734
99.7%
2024-08-08T20:29:39.518492image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16899085
39.9%
3 4895901
 
11.6%
- 3877779
 
9.2%
; 3525053
 
8.3%
2 2442022
 
5.8%
4 1646925
 
3.9%
6 1623550
 
3.8%
5 1594398
 
3.8%
1 1546708
 
3.7%
7 1482186
 
3.5%
Other values (2) 2769236
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42302843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16899085
39.9%
3 4895901
 
11.6%
- 3877779
 
9.2%
; 3525053
 
8.3%
2 2442022
 
5.8%
4 1646925
 
3.9%
6 1623550
 
3.8%
5 1594398
 
3.8%
1 1546708
 
3.7%
7 1482186
 
3.5%
Other values (2) 2769236
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42302843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16899085
39.9%
3 4895901
 
11.6%
- 3877779
 
9.2%
; 3525053
 
8.3%
2 2442022
 
5.8%
4 1646925
 
3.9%
6 1623550
 
3.8%
5 1594398
 
3.8%
1 1546708
 
3.7%
7 1482186
 
3.5%
Other values (2) 2769236
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42302843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16899085
39.9%
3 4895901
 
11.6%
- 3877779
 
9.2%
; 3525053
 
8.3%
2 2442022
 
5.8%
4 1646925
 
3.9%
6 1623550
 
3.8%
5 1594398
 
3.8%
1 1546708
 
3.7%
7 1482186
 
3.5%
Other values (2) 2769236
 
6.5%

n_offense
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9771085
Minimum0
Maximum21
Zeros36160
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:39.608292image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median11
Q311
95-th percentile11
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1971796
Coefficient of variation (CV)0.32045153
Kurtosis5.8295555
Mean9.9771085
Median Absolute Deviation (MAD)0
Skewness-2.794268
Sum3879878
Variance10.221958
MonotonicityNot monotonic
2024-08-08T20:29:39.699375image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
11 346545
89.1%
0 36160
 
9.3%
10 3069
 
0.8%
12 3065
 
0.8%
9 27
 
< 0.1%
13 5
 
< 0.1%
21 1
 
< 0.1%
18 1
 
< 0.1%
5 1
 
< 0.1%
20 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0 36160
 
9.3%
5 1
 
< 0.1%
8 1
 
< 0.1%
9 27
 
< 0.1%
10 3069
 
0.8%
11 346545
89.1%
12 3065
 
0.8%
13 5
 
< 0.1%
14 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
14 1
 
< 0.1%
13 5
 
< 0.1%
12 3065
 
0.8%
11 346545
89.1%
10 3069
 
0.8%
9 27
 
< 0.1%

n_defense
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9717109
Minimum0
Maximum22
Zeros36152
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:39.788457image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median11
Q311
95-th percentile11
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1959559
Coefficient of variation (CV)0.32050226
Kurtosis5.8253892
Mean9.9717109
Median Absolute Deviation (MAD)0
Skewness-2.7913693
Sum3877779
Variance10.214134
MonotonicityNot monotonic
2024-08-08T20:29:39.881542image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11 345814
88.9%
0 36152
 
9.3%
10 4266
 
1.1%
12 2389
 
0.6%
9 211
 
0.1%
8 18
 
< 0.1%
13 13
 
< 0.1%
22 7
 
< 0.1%
14 3
 
< 0.1%
18 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 36152
 
9.3%
8 18
 
< 0.1%
9 211
 
0.1%
10 4266
 
1.1%
11 345814
88.9%
12 2389
 
0.6%
13 13
 
< 0.1%
14 3
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
22 7
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 3
 
< 0.1%
13 13
 
< 0.1%
12 2389
 
0.6%
11 345814
88.9%

ngs_air_yards
Real number (ℝ)

MISSING 

Distinct6693
Distinct (%)4.6%
Missing243814
Missing (%)62.7%
Infinite0
Infinite (%)0.0%
Mean8.159541
Minimum-17.55
Maximum65
Zeros311
Zeros (%)0.1%
Negative25762
Negative (%)6.6%
Memory size3.0 MiB
2024-08-08T20:29:39.990762image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-17.55
5-th percentile-4.11
Q11.73
median5.53
Q312.91
95-th percentile28.3085
Maximum65
Range82.55
Interquartile range (IQR)11.18

Descriptive statistics

Standard deviation9.9862227
Coefficient of variation (CV)1.2238706
Kurtosis2.3961596
Mean8.159541
Median Absolute Deviation (MAD)5.26
Skewness1.3616648
Sum1183655.7
Variance99.724643
MonotonicityNot monotonic
2024-08-08T20:29:40.109753image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311
 
0.1%
4 157
 
< 0.1%
5 157
 
< 0.1%
4.56 139
 
< 0.1%
4.77 138
 
< 0.1%
4.69 133
 
< 0.1%
5.08 133
 
< 0.1%
5.19 129
 
< 0.1%
4.61 128
 
< 0.1%
4.8 128
 
< 0.1%
Other values (6683) 143511
36.9%
(Missing) 243814
62.7%
ValueCountFrequency (%)
-17.55 1
< 0.1%
-14.45 1
< 0.1%
-14.27 1
< 0.1%
-14.11 1
< 0.1%
-13.84 1
< 0.1%
-13.82 1
< 0.1%
-13.16 1
< 0.1%
-13.08 1
< 0.1%
-13 1
< 0.1%
-12.86 1
< 0.1%
ValueCountFrequency (%)
65 1
< 0.1%
64.75 1
< 0.1%
64.35 1
< 0.1%
63.52 1
< 0.1%
63.36 1
< 0.1%
63.11 1
< 0.1%
62.7 1
< 0.1%
62.27 1
< 0.1%
62.21 1
< 0.1%
61.43 1
< 0.1%

time_to_throw
Real number (ℝ)

MISSING 

Distinct5615
Distinct (%)3.8%
Missing240418
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean2.7452011
Minimum0.033
Maximum14.281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 MiB
2024-08-08T20:29:40.222854image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.033
5-th percentile1.435
Q12.089
median2.57
Q33.17
95-th percentile4.705
Maximum14.281
Range14.248
Interquartile range (IQR)1.081

Descriptive statistics

Standard deviation1.0284568
Coefficient of variation (CV)0.37463804
Kurtosis4.9383943
Mean2.7452011
Median Absolute Deviation (MAD)0.533
Skewness1.5835081
Sum407552.56
Variance1.0577233
MonotonicityNot monotonic
2024-08-08T20:29:40.339217image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.636 1535
 
0.4%
2.269 1506
 
0.4%
2.469 1500
 
0.4%
2.536 1490
 
0.4%
2.369 1484
 
0.4%
2.736 1410
 
0.4%
2.002 1406
 
0.4%
2.569 1385
 
0.4%
2.436 1349
 
0.3%
2.102 1347
 
0.3%
Other values (5605) 134048
34.5%
(Missing) 240418
61.8%
ValueCountFrequency (%)
0.033 1
< 0.1%
0.048 1
< 0.1%
0.098 1
< 0.1%
0.106 1
< 0.1%
0.116 1
< 0.1%
0.196 1
< 0.1%
0.231 2
< 0.1%
0.264 2
< 0.1%
0.297 1
< 0.1%
0.299 1
< 0.1%
ValueCountFrequency (%)
14.281 1
< 0.1%
13.28 1
< 0.1%
12.846 1
< 0.1%
12.579 1
< 0.1%
12.323 1
< 0.1%
12.112 1
< 0.1%
11.97 1
< 0.1%
11.94 1
< 0.1%
11.678 1
< 0.1%
11.444 1
< 0.1%

was_pressure
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing240432
Missing (%)61.8%
Memory size3.0 MiB
False
106367 
True
42079 
(Missing)
240432 
ValueCountFrequency (%)
False 106367
27.4%
True 42079
 
10.8%
(Missing) 240432
61.8%
2024-08-08T20:29:40.440627image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

route
Categorical

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing245106
Missing (%)63.0%
Memory size3.0 MiB
HITCH
23112 
FLAT
20227 
GO
17615 
OUT
17551 
CROSS
16784 
Other values (7)
48483 

Length

Max length6
Median length5
Mean length4.1649278
Min length2

Characters and Unicode

Total characters598800
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSCREEN
2nd rowFLAT
3rd rowOUT
4th rowGO
5th rowOUT

Common Values

ValueCountFrequency (%)
HITCH 23112
 
5.9%
FLAT 20227
 
5.2%
GO 17615
 
4.5%
OUT 17551
 
4.5%
CROSS 16784
 
4.3%
SCREEN 12813
 
3.3%
SLANT 9983
 
2.6%
IN 7406
 
1.9%
POST 7207
 
1.9%
ANGLE 5605
 
1.4%
Other values (2) 5469
 
1.4%
(Missing) 245106
63.0%

Length

2024-08-08T20:29:40.542444image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hitch 23112
16.1%
flat 20227
14.1%
go 17615
12.3%
out 17551
12.2%
cross 16784
11.7%
screen 12813
8.9%
slant 9983
6.9%
in 7406
 
5.2%
post 7207
 
5.0%
angle 5605
 
3.9%
Other values (2) 5469
 
3.8%

Most occurring characters

ValueCountFrequency (%)
T 78080
13.0%
O 63883
10.7%
S 63571
10.6%
C 57435
9.6%
H 46967
7.8%
N 40533
6.8%
R 39049
 
6.5%
E 37443
 
6.3%
L 36558
 
6.1%
A 35815
 
6.0%
Other values (6) 99466
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 598800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 78080
13.0%
O 63883
10.7%
S 63571
10.6%
C 57435
9.6%
H 46967
7.8%
N 40533
6.8%
R 39049
 
6.5%
E 37443
 
6.3%
L 36558
 
6.1%
A 35815
 
6.0%
Other values (6) 99466
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 598800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 78080
13.0%
O 63883
10.7%
S 63571
10.6%
C 57435
9.6%
H 46967
7.8%
N 40533
6.8%
R 39049
 
6.5%
E 37443
 
6.3%
L 36558
 
6.1%
A 35815
 
6.0%
Other values (6) 99466
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 598800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 78080
13.0%
O 63883
10.7%
S 63571
10.6%
C 57435
9.6%
H 46967
7.8%
N 40533
6.8%
R 39049
 
6.5%
E 37443
 
6.3%
L 36558
 
6.1%
A 35815
 
6.0%
Other values (6) 99466
16.6%

defense_man_zone_type
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing277692
Missing (%)71.4%
Memory size3.0 MiB
ZONE_COVERAGE
75269 
MAN_COVERAGE
35917 

Length

Max length13
Median length13
Mean length12.676965
Min length12

Characters and Unicode

Total characters1409501
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZONE_COVERAGE
2nd rowZONE_COVERAGE
3rd rowZONE_COVERAGE
4th rowZONE_COVERAGE
5th rowMAN_COVERAGE

Common Values

ValueCountFrequency (%)
ZONE_COVERAGE 75269
 
19.4%
MAN_COVERAGE 35917
 
9.2%
(Missing) 277692
71.4%

Length

2024-08-08T20:29:40.653545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:29:40.736620image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
zone_coverage 75269
67.7%
man_coverage 35917
32.3%

Most occurring characters

ValueCountFrequency (%)
E 297641
21.1%
O 186455
13.2%
A 147103
10.4%
N 111186
 
7.9%
_ 111186
 
7.9%
C 111186
 
7.9%
V 111186
 
7.9%
R 111186
 
7.9%
G 111186
 
7.9%
Z 75269
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1409501
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 297641
21.1%
O 186455
13.2%
A 147103
10.4%
N 111186
 
7.9%
_ 111186
 
7.9%
C 111186
 
7.9%
V 111186
 
7.9%
R 111186
 
7.9%
G 111186
 
7.9%
Z 75269
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1409501
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 297641
21.1%
O 186455
13.2%
A 147103
10.4%
N 111186
 
7.9%
_ 111186
 
7.9%
C 111186
 
7.9%
V 111186
 
7.9%
R 111186
 
7.9%
G 111186
 
7.9%
Z 75269
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1409501
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 297641
21.1%
O 186455
13.2%
A 147103
10.4%
N 111186
 
7.9%
_ 111186
 
7.9%
C 111186
 
7.9%
V 111186
 
7.9%
R 111186
 
7.9%
G 111186
 
7.9%
Z 75269
 
5.3%

defense_coverage_type
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing277692
Missing (%)71.4%
Memory size3.0 MiB
COVER_3
35375 
COVER_1
28633 
COVER_4
15807 
COVER_2
14872 
COVER_6
8704 
Other values (3)
7795 

Length

Max length7
Median length7
Mean length6.9593654
Min length5

Characters and Unicode

Total characters773784
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOVER_3
2nd rowCOVER_3
3rd rowCOVER_3
4th rowCOVER_3
5th rowCOVER_0

Common Values

ValueCountFrequency (%)
COVER_3 35375
 
9.1%
COVER_1 28633
 
7.4%
COVER_4 15807
 
4.1%
COVER_2 14872
 
3.8%
COVER_6 8704
 
2.2%
COVER_0 5025
 
1.3%
2_MAN 2259
 
0.6%
PREVENT 511
 
0.1%
(Missing) 277692
71.4%

Length

2024-08-08T20:29:40.835710image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-08T20:29:40.941806image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
cover_3 35375
31.8%
cover_1 28633
25.8%
cover_4 15807
14.2%
cover_2 14872
13.4%
cover_6 8704
 
7.8%
cover_0 5025
 
4.5%
2_man 2259
 
2.0%
prevent 511
 
0.5%

Most occurring characters

ValueCountFrequency (%)
_ 110675
14.3%
E 109438
14.1%
V 108927
14.1%
R 108927
14.1%
C 108416
14.0%
O 108416
14.0%
3 35375
 
4.6%
1 28633
 
3.7%
2 17131
 
2.2%
4 15807
 
2.0%
Other values (7) 22039
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 773784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 110675
14.3%
E 109438
14.1%
V 108927
14.1%
R 108927
14.1%
C 108416
14.0%
O 108416
14.0%
3 35375
 
4.6%
1 28633
 
3.7%
2 17131
 
2.2%
4 15807
 
2.0%
Other values (7) 22039
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 773784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 110675
14.3%
E 109438
14.1%
V 108927
14.1%
R 108927
14.1%
C 108416
14.0%
O 108416
14.0%
3 35375
 
4.6%
1 28633
 
3.7%
2 17131
 
2.2%
4 15807
 
2.0%
Other values (7) 22039
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 773784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 110675
14.3%
E 109438
14.1%
V 108927
14.1%
R 108927
14.1%
C 108416
14.0%
O 108416
14.0%
3 35375
 
4.6%
1 28633
 
3.7%
2 17131
 
2.2%
4 15807
 
2.0%
Other values (7) 22039
 
2.8%

Interactions

2024-08-08T20:29:31.729783image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:25.776004image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.713954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.686476image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.512103image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.311880image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.154229image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.973918image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.838887image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:25.919092image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.839073image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.806619image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.619207image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.435999image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.278872image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.081019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.937981image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.044208image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.950179image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.914825image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.718299image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.542281image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.386971image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.174108image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:32.032077image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.147301image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.048275image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.010232image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.812386image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.638387image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.480063image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.266195image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:32.130170image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.271415image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.162524image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.118332image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.906473image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.748852image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.595222image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.356278image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:32.227261image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.388521image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.271629image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.226832image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.008568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.854951image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.699323image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.452820image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:32.447467image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.487614image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.365720image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.322924image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.104655image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.943031image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.785317image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.543302image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:32.537552image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:26.585737image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:27.457807image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:28.412009image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:29.194766image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.034116image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:30.874292image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-08T20:29:31.629685image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-08-08T20:29:41.033877image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
defenders_in_boxdefense_coverage_typedefense_man_zone_typen_defensen_offensengs_air_yardsnumber_of_pass_rushersoffense_formationold_game_idplay_idpossession_teamroutetime_to_throwwas_pressure
defenders_in_box1.0000.2300.1800.0030.003-0.0060.2380.351-0.026-0.0580.0430.0560.0390.014
defense_coverage_type0.2301.0001.0000.0261.0000.0810.2180.0950.0390.0580.0470.0970.0500.063
defense_man_zone_type0.1801.0001.0000.0071.0000.1360.3870.0710.0700.0220.0600.2240.0780.053
n_defense0.0030.0260.0071.0000.865-0.0010.0100.013-0.005-0.0740.1270.0050.0080.002
n_offense0.0031.0001.0000.8651.000-0.003-0.0040.024-0.015-0.0750.0570.0270.0100.009
ngs_air_yards-0.0060.0810.136-0.001-0.0031.0000.0700.051-0.0310.0510.0270.3470.3800.127
number_of_pass_rushers0.2380.2180.3870.010-0.0040.0701.0000.1040.017-0.0160.0280.064-0.0340.137
offense_formation0.3510.0950.0710.0130.0240.0510.1041.0000.0470.0530.1380.0970.0930.026
old_game_id-0.0260.0390.070-0.005-0.015-0.0310.0170.0471.000-0.0040.0970.0670.0460.050
play_id-0.0580.0580.022-0.074-0.0750.051-0.0160.053-0.0041.0000.0180.0300.0300.038
possession_team0.0430.0470.0600.1270.0570.0270.0280.1380.0970.0181.0000.0440.0450.056
route0.0560.0970.2240.0050.0270.3470.0640.0970.0670.0300.0441.0000.1720.151
time_to_throw0.0390.0500.0780.0080.0100.380-0.0340.0930.0460.0300.0450.1721.0000.300
was_pressure0.0140.0630.0530.0020.0090.1270.1370.0260.0500.0380.0560.1510.3001.000

Missing values

2024-08-08T20:29:32.772773image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-08T20:29:33.341391image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-08T20:29:34.502985image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nflverse_game_idold_game_idplay_idpossession_teamoffense_formationoffense_personneldefenders_in_boxdefense_personnelnumber_of_pass_rushersplayers_on_playoffense_playersdefense_playersn_offensen_defensengs_air_yardstime_to_throwwas_pressureroutedefense_man_zone_typedefense_coverage_type
02016_01_CAR_DEN20160908001NaNNaNNaNNaNNaNNaNNaNNaNNaN00NaNNaNNaNNaNNaNNaN
12016_01_CAR_DEN201609080036CARNaNNaNNaNNaNNaN41285;42728;39857;43366;35656;42512;41916;40096;44017;34659;36494;41915;37841;35638;35102;43465;41471;43508;35685;42727;43387;3726500-0029731;00-0032891;00-0031596;00-0030463;00-0032994;00-0027853;00-0028339;00-0027796;00-0026858;00-0027835;00-002812800-0031344;00-0031697;00-0027812;00-0031102;00-0027139;00-0031101;00-0032956;00-0031350;00-0032958;00-0031696;00-00329711111NaNNaNNaNNaNNaNNaN
22016_01_CAR_DEN201609080051DENSINGLEBACK1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DB4.035521;42593;41377;35685;35461;41289;35690;38539;38604;41324;42476;41357;29839;34961;32276;35445;40311;43351;39960;39992;41436;3727900-0027685;00-0032156;00-0027874;00-0029543;00-0031353;00-0032132;00-0026739;00-0027859;00-0029854;00-0031349;00-002814200-0031262;00-0027835;00-0031336;00-0027840;00-0029248;00-0031261;00-0023448;00-0025470;00-0032890;00-0030455;00-00304481111-1.212.323FalseSCREENNaNNaN
32016_01_CAR_DEN201609080075DENI_FORM6 OL, 2 RB, 0 TE, 2 WR8.04 DL, 3 LB, 4 DB6.042368;41377;42593;35521;41285;35685;41289;43465;38539;38604;41324;42476;41357;29839;39027;32276;35445;40311;43351;39960;39992;4143600-0032156;00-0027685;00-0031344;00-0032956;00-0029543;00-0031353;00-0032132;00-0027859;00-0029854;00-003134900-0031550;00-0031262;00-0027835;00-0031336;00-0029248;00-0031261;00-0023448;00-0025470;00-0032890;00-0030455;00-00304481011-2.222.893TrueFLATNaNNaN
42016_01_CAR_DEN201609080097DENSINGLEBACK1 RB, 1 TE, 3 WR7.04 DL, 2 LB, 5 DB3.041285;42476;43351;38539;39992;38604;39960;41377;40311;35521;41436;35445;41357;41324;35685;32276;29839;35690;42593;34961;41289;3727900-0031344;00-0032132;00-0029543;00-0029854;00-0027685;00-0031349;00-0027859;00-0031353;00-0032156;00-0026739;00-002814200-0032890;00-0029248;00-0030448;00-0030455;00-0031262;00-0031261;00-0027835;00-0025470;00-0023448;00-0027840;00-003133611114.912.556FalseOUTNaNNaN
52016_01_CAR_DEN2016090800119DENSHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DB4.041285;43351;42476;38539;39992;39960;38604;41377;40311;37841;35521;41436;35445;41357;35685;41324;32276;29839;35690;42593;34961;3727900-0031344;00-0032132;00-0029543;00-0029854;00-0027685;00-0031349;00-0027859;00-0031353;00-0032156;00-0026739;00-002814200-0032890;00-0029248;00-0030448;00-0030455;00-0031262;00-0028339;00-0031261;00-0027835;00-0025470;00-0023448;00-002784011119.744.590FalseGONaNNaN
62016_01_CAR_DEN2016090800143DENSINGLEBACK1 RB, 0 TE, 4 WR6.04 DL, 2 LB, 5 DB5.042728;41285;42476;43351;38539;38604;41377;40311;37841;42874;35521;41436;43319;35445;41357;41324;35685;29839;35690;42593;34961;3230100-0031697;00-0031344;00-0032132;00-0029543;00-0029854;00-0027685;00-0031349;00-0027859;00-0031353;00-0032156;00-002673900-0032890;00-0029248;00-0031262;00-0028339;00-0031843;00-0032967;00-0031261;00-0027835;00-0023448;00-0027840;00-002549511114.051.502FalseOUTNaNNaN
72016_01_CAR_DEN2016090800167DENI_FORM2 RB, 1 TE, 2 WR7.04 DL, 3 LB, 4 DBNaN42728;41285;43351;42476;38539;38604;41377;40311;37841;42874;43319;41436;43465;35445;41357;35685;41324;29839;42593;42368;32301;3727900-0031697;00-0031344;00-0032132;00-0029543;00-0029854;00-0031349;00-0032956;00-0027859;00-0031353;00-0032156;00-002814200-0032890;00-0029248;00-0031262;00-0028339;00-0031843;00-0032967;00-0031261;00-0027835;00-0023448;00-0031550;00-00254951111NaNNaNNaNNaNNaNNaN
82016_01_CAR_DEN2016090800188DENSINGLEBACK1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DBNaN43351;42476;38539;38604;41377;40311;37841;42874;43319;35521;41436;35445;41357;35685;41324;29839;35690;35461;42593;34961;32301;3727900-0032132;00-0029543;00-0029854;00-0027685;00-0031349;00-0027859;00-0031353;00-0027874;00-0032156;00-0026739;00-002814200-0032890;00-0029248;00-0031262;00-0028339;00-0031843;00-0032967;00-0031261;00-0027835;00-0023448;00-0027840;00-00254951111NaNNaNNaNNaNNaNNaN
92016_01_CAR_DEN2016090800209DENNaN2 RB, 1 TE, 2 WR7.04 DL, 3 LB, 4 DBNaN42368;35521;41377;42593;43425;34659;35461;35685;41289;43465;38539;38604;41324;42476;41357;29839;32276;35445;43351;39960;39992;4143600-0027685;00-0032156;00-0032972;00-0027139;00-0027874;00-0032956;00-0029543;00-0031353;00-0032132;00-0027859;00-003134900-0031550;00-0031262;00-0027835;00-0031336;00-0029248;00-0031261;00-0023448;00-0025470;00-0032890;00-0030455;00-00304481111NaNNaNNaNNaNNaNNaN
nflverse_game_idold_game_idplay_idpossession_teamoffense_formationoffense_personneldefenders_in_boxdefense_personnelnumber_of_pass_rushersplayers_on_playoffense_playersdefense_playersn_offensen_defensengs_air_yardstime_to_throwwas_pressureroutedefense_man_zone_typedefense_coverage_type
3888682023_22_SF_KC20240211004684KCSHOTGUN1 RB, 1 TE, 3 WR6.03 DL, 3 LB, 5 DB5.053601;46243;42403;46213;46757;52422;47785;42377;47818;40011;46157;55920;55952;53492;38868;44822;53655;46139;48123;54716;47999;4003100-0034272;00-0034386;00-0032217;00-0035237;00-0030506;00-0039067;00-0036623;00-0033873;00-0036660;00-0037197;00-003514900-0036563;00-0032197;00-0034573;00-0036260;00-0035717;00-0034754;00-0038554;00-0028924;00-0034815;00-0035026;00-003041111112.621.501FalseHITCHMAN_COVERAGECOVER_0
3888692023_22_SF_KC20240211004709KCSHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DB7.053601;46243;46757;46309;52422;42377;47785;47818;40011;46157;41325;55920;55952;38868;53492;44822;53655;42360;52410;46139;47999;4003100-0034272;00-0034286;00-0032217;00-0035237;00-0030506;00-0031376;00-0039067;00-0036623;00-0033873;00-0036660;00-003514900-0036563;00-0034573;00-0036260;00-0035717;00-0034754;00-0038554;00-0028924;00-0032164;00-0036321;00-0034815;00-003041111112.332.502TrueCROSSMAN_COVERAGECOVER_0
3888702023_22_SF_KC20240211004734KCSINGLEBACK1 RB, 3 TE, 1 WR7.03 DL, 3 LB, 5 DB4.053601;42403;46757;47785;42377;47818;40011;46157;55920;55952;43378;53492;38868;44822;53591;53655;42360;46139;42460;54716;47999;4003100-0032217;00-0035237;00-0030506;00-0039067;00-0036623;00-0033873;00-0036637;00-0036660;00-0032062;00-0037197;00-003514900-0036563;00-0032197;00-0034573;00-0035717;00-0034754;00-0038554;00-0033109;00-0028924;00-0032164;00-0034815;00-00304111111-3.224.438TrueANGLEZONE_COVERAGECOVER_4
3888712023_22_SF_KC20240211004759KCSHOTGUN1 RB, 1 TE, 3 WR6.03 DL, 3 LB, 5 DB4.053601;46243;42403;46757;46213;47785;42377;47818;40011;46157;55920;55952;43378;38868;53492;44822;53655;46139;48123;54716;47999;4003100-0034272;00-0034386;00-0032217;00-0035237;00-0030506;00-0039067;00-0036623;00-0033873;00-0036660;00-0037197;00-003514900-0036563;00-0032197;00-0034573;00-0035717;00-0034754;00-0038554;00-0033109;00-0028924;00-0034815;00-0035026;00-00304111111-4.541.935FalseSCREENMAN_COVERAGECOVER_1
3888722023_22_SF_KC20240211004771NaNNaNNaNNaNNaNNaNNaNNaNNaN00NaNNaNNaNNaNNaNNaN
3888732023_22_SF_KC20240211004791KCSHOTGUN1 RB, 2 TE, 2 WR5.04 DL, 2 LB, 5 DB4.053601;46243;46757;47785;42377;47818;40011;46157;55920;55952;53492;38868;44822;53591;53655;42360;52410;46139;48123;54716;47999;4003100-0034272;00-0032217;00-0035237;00-0030506;00-0039067;00-0036623;00-0033873;00-0036637;00-0036660;00-0037197;00-003514900-0036563;00-0034573;00-0035717;00-0034754;00-0038554;00-0028924;00-0032164;00-0036321;00-0034815;00-0035026;00-00304111111NaNNaNNaNNaNNaNNaN
3888742023_22_SF_KC20240211004813KCSHOTGUN1 RB, 3 TE, 1 WR6.04 DL, 2 LB, 5 DBNaN53601;46213;46757;47785;42377;47818;40011;46157;55952;38868;53492;44822;53655;53591;42360;52410;46139;48123;42460;54716;40031;4799900-0034386;00-0032217;00-0035237;00-0030506;00-0036623;00-0033873;00-0036660;00-0036637;00-0032062;00-0037197;00-003514900-0036563;00-0034573;00-0035717;00-0034754;00-0038554;00-0028924;00-0032164;00-0036321;00-0034815;00-0035026;00-00304111111NaNNaNNaNNaNNaNNaN
3888752023_22_SF_KC20240211004835KCSHOTGUN1 RB, 1 TE, 3 WR6.04 DL, 2 LB, 5 DB4.053601;46243;46213;46757;52422;47785;42377;47818;40011;46157;55920;55952;43378;53492;38868;44822;53655;52410;46139;54716;47999;4003100-0034272;00-0034386;00-0032217;00-0035237;00-0030506;00-0039067;00-0036623;00-0033873;00-0036660;00-0037197;00-003514900-0036563;00-0034573;00-0036260;00-0035717;00-0034754;00-0038554;00-0033109;00-0028924;00-0036321;00-0034815;00-00304111111-3.172.069FalseSCREENZONE_COVERAGECOVER_4
3888762023_22_SF_KC20240211004860KCSHOTGUN1 RB, 2 TE, 2 WR8.04 DL, 2 LB, 5 DB4.053601;46243;46757;52422;47785;42377;47818;40011;46157;41325;55952;43378;53492;38868;44822;53591;53655;52410;46139;47999;47839;4003100-0034272;00-0032217;00-0035237;00-0030506;00-0031376;00-0036623;00-0033873;00-0036637;00-0036660;00-0035149;00-003514000-0036563;00-0034573;00-0036260;00-0035717;00-0034754;00-0038554;00-0033109;00-0028924;00-0036321;00-0034815;00-003041111111.412.236FalseFLATMAN_COVERAGECOVER_0
3888772023_22_SF_KC20240211004881NaNNaNNaNNaNNaNNaNNaNNaNNaN00NaNNaNNaNNaNNaNNaN